Recently, with the rapid development of mobile sensing technology, capturing scene information by mobile sensing devices in the form of images or videos has become a prevalent recording method. However, the moiré pattern phenomenon may occur when the scene contains digital screens or regular strips, which greatly degrade the visual performance and image quality. In this paper, considering the complexity and diversity of moiré patterns, we propose a novel end-to-end image demoiré method, which can learn moiré pattern elimination in both the frequency and spatial domains. To be specific, in the frequency domain, considering the signal energy of moiré pattern is widely distributed in the frequency, we introduce a wavelet transform to decompose the multi-scale image features, which can help the model identify the moiré features more precisely to suppress them effectively. On the other hand, we also design a spatial domain demoiré block (SDDB). The SDDB module can extract moiré features from the mixed features, then subtract them to obtain clean image features. The combination of the frequency domain and the spatial domain enhances the model’s ability in terms of moiré feature recognition and elimination. Finally, extensive experiments demonstrate the superior performance of our proposed method to other state-of-the-art methods. The Grad-CAM results in our ablation study fully indicate the effectiveness of the two proposed blocks in our method.

Most of the existing low-light enhancement research focuses on global illumination enhancement while ignoring the issues of brightness unevenness and color distortion. To address this dilemma, we propose a low-light image enhancement method that can achieve good performance in luminance adjustment and color correction simultaneously. Specifically, the Luminance Adjustment Module is designed to model the global luminance adjustment parameters while taking into account the relationship between global and local illumination features, in order to prevent overexposure or underexposure. Furthermore, we design a Color Correction Module based on the attention mechanism, which utilizes the attention mechanism to capture global color features and correct the color deviation in the illumination-enhanced image. Additionally, we design a color loss function based on a 14-dimensional statistical feature vector related to color, enabling further restoration of the image’s true color. We conduct empirical studies on multiple public low-light datasets, demonstrating that the proposed method outperforms other representative state-of-the-art models regarding illumination enhancement and color correction.

With the growth of urban population, a series of urban problems have emerged, and how to speed up smart city construction has received extensive attention. Remote sensing images have the advantages of wide spatial coverage and rich information, and it is suitable for use as research data for smart cities. However, due to limitations in the imaging sensor conditions and complex weather, remote sensing images face the problems of insufficient resolution and cloud occlusion, which cannot meet the resolution requirements of smart city tasks. The remote sensing image super-resolution (SR) technique can improve the details and texture information without upgrading the imaging sensor system, which becomes a feasible solution for the above problems. In this paper, we propose a novel remote sensing image super-resolution method which leverages the texture features from internal and external references to help with SR reconstruction. We introduce the transformer attention mechanism to select and extract parts of texture features with high reference values to ensure that the network is lightweight, effective, and easier to deploy on edge computing devices. In addition, our network can automatically learn and adjust the alignment angles and scales of texture features for better SR results. Extensive comparison experiments show that our proposed method achieves superior performance compared with several state-of-the-art SR methods. In addition, we also evaluate the application value of our proposed SR method in urban region function recognition in smart cities. The dataset used in this task is low-quality. The comparative experiment between the original dataset and the SR dataset generated by our proposed SR method indicates that our method can effectively improve the recognition accuracy.